A Novel 3D Statistical Shape Model for Segmentation of Medical Images

  • Zheen Zhao
  • Eam Khwang Teoh
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4291)


A 3D Partitioned Active Shape Model (PASM) is proposed in this paper to address the problems of 3D Active Shape Models (ASM) caused by the limited numbers of training samples, which is usually the case in 3D segmentation. When training sets are small, 3D ASMs tend to be restrictive, because the plausible area/allowable region spanned by relatively few eigenvectors cannot capture the full range of shape variability. 3D PASMs overcome this limitation by using a partitioned representation of the ASM. Given a Point Distribution Model (PDM), the mean mesh is partitioned into a group of small tiles. The statistical priors of tiles are estimated by applying Principal Component Analysis to each tile to constrain corresponding tiles during deformation. To avoid the inconsistency of shapes between tiles, samples are projected as curves in one hyperspace, instead of point clouds in several hyperspaces. The deformed model points are then fitted into the allowable region of the model by using a curve alignment scheme. The experiments on 3D human brain MRIs show that when the numbers of the training samples are limited, the 3D PASMs significantly improve the segmentation results as compared to 3D ASMs and 3D Hierarchical ASMs, which are the extension of the 2D Hierarchical ASM to the 3D case.


Point Cloud Training Sample Manual Segmentation Deformable Model Active Contour Model 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Zheen Zhao
    • 1
    • 2
  • Eam Khwang Teoh
    • 2
  1. 1.Department of PsychiatryDuke UniversityDurhamUSA
  2. 2.School of Electrical & Electronics EngineeringNanyang Technological UniversitySingapore

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